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The microscopic properties that determine hygroscopic behavior are complex. The importance of hygroscopicity to many areas, and particularly atmospheric chemistry, in terms of aerosol growth and cloud nucleation, mandate the need for robust models to understand this behavior. Toward this end, we have employed molecular dynamics simulations to calculate hygroscopicity from atomistic models using free energy perturbation. We find that currently available force fields may not be well-suited to modeling the extreme environments of aerosol particles. Nonetheless, the results illuminate some shortcomings in our current understanding of hygroscopic growth and cloud nucleation. The most widely used model of hygroscopicity, κ-Köhler Theory (κKT), breaks down in the case of deviations from ideal solution behavior and empirical adjustments within the simplified framework cannot account for non-ideal behavior. A revised model that incorporates non-ideal mixing rescues the general framework of κKT and allows us to understand our simulation results as well as the behavior of atmospheric aerosols over the full range of humidity. The revised model shows that non-ideal mixing dominates hygroscopic growth at subsaturation humidity. Thus, a model based on ideal mixing will fail to predict subsaturation growth from cloud condensation nucleus (CCN) activation or vice versa ; a single parameter model for hygroscopicity will generally be insufficient to extrapolate across wide ranges of humidity. We argue that in many cases, when data are limited to subsaturation humidity, an empirical model for non-ideal mixing may be more successful than one for ideal mixing. 

We have developed Differential Specificity and Energy Landscape (DiSEL) analysis to comprehensively compare DNA–protein interactomes (DPIs) obtained by high-throughput experimental platforms and cutting edge computational methods. While high-affinity DNA binding sites are identified by most methods, DiSEL uncovered nuanced sequence preferences displayed by homologous transcription factors. Pairwise analysis of 726 DPIs uncovered homolog-specific differences at moderate- to low-affinity binding sites (submaximal sites). DiSEL analysis of variants of 41 transcription factors revealed that many disease-causing mutations result in allele-specific changes in binding site preferences. We focused on a set of highly homologous factors that have different biological roles but “read” DNA using identical amino acid side chains. Rather than direct readout, our results indicate that DNA noncontacting side chains allosterically contribute to sculpt distinct sequence preferences among closely related members of transcription factor families.